Fuzz testing is a widely used method for finding security issues in software. However, certain code paths can only be explored under specific program states. Flag variables, which represent internal states, are crucial in influencing program behavior through flag-guarded branches. Unfortunately, existing fuzzing tools struggle to efficiently explore them due to the implicit data dependency between flag variables and the input. As a result, they commonly lack awareness of the dependency between program input and the assignments of critical flag variables, leading to a blind or random approach to satisfy flag-checking constraints, which greatly impacts the fuzzing efficiency.To address this issue, this paper proposes a dynamic flag-guided hybrid fuzzing approach, which automates the identification of flag variables and provides guidance for fuzz testing. Specifically, we first design a pre-fuzzing program analysis to recognize flag variables and a novel data structure to present how flag variables guard code branches. Then, we propose a new constraint-solving approach by separating complex flag-checking constraints into a set of atomic ones and sequentially solving them by traversing our FDG to locate execution paths that could assign the flag variables with the desired values.We implement a prototype tool, called Algernon, and evaluate it on 20 popular open-source programs. Across all tested programs, Algernon outperforms QSYM, Angora, AFL++, and INVSCOV in terms of both code coverage and vulnerability discovery, demonstrating the effectiveness of our approach. During our experiments, Algernon successfully found 30 zero-day vulnerabilities with 11 CVE IDs assigned.

This paper studies a new technique that characterizes a speaker by the difference between the speaker and a cohort of background speakers in the form of feature-space maximum a posteriori linear regression (fMAPLR). The fMAPLR is a linear regression function that projects speaker dependent features to speaker independent ones, also known as an affine transform. It consists of two sets of parameters, bias vectors and transform matrices. The former, representing the first order information, is more robust than the latter, the second-order information. We propose a flexible tying scheme that allows the bias vectors and the matrices to be associated with different regression classes, such that both parameters are given sufficient statistics in a speaker verification task. We formulate a maximum a posteriori (MAP) algorithm for the estimation of feature transform parameters, that further alleviates the possible numerical problem. The fMAPLR parameters are then vectorized and compared via a support vector machine (SVM). We conduct the experiments on National Institute of Standards and Technology (NIST) 2006 and 2008 Speaker Recognition Evaluation databases. The experiments show that the proposed technique consistently outperforms the baseline Gaussian mixture model (GMM)-SVM speaker verification system.

This paper extends our previous work on large margin estimation (LME) of GMM parameters with extend Baum-Welch (EBW) for spoken language recognition. To overcome the problem in the LME that negative samples in the training set are not used in parameter estimation, we propose a soft margin estimation (SME) method in this paper. The soft margin is scaled by a loss function measuring the distance between a negative sample and the classification boundary. We formulate the constrained optimization of SME as an unconstrained optimization among both positive samples and negative samples using a penalty function, and update the GMM parameters with the EBW algorithm. Experiments on the NIST language recognition evaluation (LRE) 2007 task show that the SME method effectively improves the LME performance.

We present a new approach to robust speech recognition based on structured modeling, irrelevant variability normalization (IVN) and unsupervised online adaptation (OLA). In offline training stage, a set of generic HMMs for basic speech units relevant to phonetic classification is trained along with several sets of feature transforms with different degrees of freedom by using a maximum likelihood (ML) IVN-based training strategy. In recognition stage, after a first-pass recognition, the most appropriate set of feature transforms is identified and adapted under ML criterion by using the unknown utterance itself, which is recognized again to achieve better performance by using the adapted feature transforms and the pre-trained generic HMMs. The effectiveness of the proposed approach is confirmed by evaluation experiments on Finnish Aurora3 database.

This paper describes the performance of the I4U speaker recognition system in the NIST 2008 Speaker Recognition Evaluation. The system consists of seven subsystems, each with different cepstral features and classifiers. We describe the I4U Primary system and report on its core test results as they were submitted, which were among the best-performing submissions. The I4U effort was led by the Institute for Infocomm Research, Singapore (IIR), with contributions from the University of Science and Technology of China (USTC), the University of New South Wales, Australia (UNSW), Nanyang Technological University, Singapore (NTU) and Carnegie Mellon University, USA (CMU).

This paper extends our previous work on feature transformation-based support vector machines for speaker recognition by proposing a joint MAP adaptation of feature transformation (FT) and Gaussian Mixture Models (GMM) parameters. In the new approach, the prior probability density functions (PDFs) of FT and GMM parameters are jointly estimated using the background data under the maximum likelihood criteria. In this way, we derive a generic prior GMM that is more compact than the Universal Background Model due to the reduction of speaker variations. With the prior PDFs, we construct a supervector to characterize a speaker using FT and GMM parameters. We conducted experiments on NIST 2006 Speaker Recognition Evaluation (SRE06) data set. The results validated the effectiveness of the joint MAP adaptation approach.

We propose a speaker recognition system based on the acoustic segment modeling technique. It is assumed that the overall sound characteristics for speakers can be covered by a set of acoustic segment models (ASMs) while the ASMs are acoustically-motivated self-organized sound units without imposing any phonetic definitions. These acoustic segment models decode a spoken utterance into a string of segment units and the mean vectors of ASMs based on the unsupervised MAP adaptation are concatenated to represent the characteristics of the specific speaker. Support vector machines are thus applied on these high dimensional feature vectors for speaker recognition. We evaluate the proposed approach in the 2006 NIST speaker recognition evaluation core condition test trials.

Discriminative training (DT) methods of acoustic models, such as SVM and MMI-training GMM, have been proved effective in spoken language recognition. In this paper we propose a DT method for GMM using the large margin (LM) estimation. Unlike traditional MMI or MCE methods, the LM estimation attempts to enhance the generalization ability of GMM to deal with new data that exhibits mismatch with training data. We define the multi-class separation margin as a function of GMM likelihoods, and derive update formulae of GMM parameters with the extended Baum-Welch algorithm. Results on the NIST language recognition evaluation (LRE) 2007 task show that the LM estimation achieves better performance and faster convergent speed than the MMI estimation.

In the past several years, we've been studying feature transformation (FT) approaches to robust automatic speech recognition (ASR) which can compensate for possible "distortions" caused by factors irrelevant to phonetic classification in both training and recognition stages. Several FT functions with different degrees of flexibility have been studied and the corresponding maximum likelihood (ML) training techniques developed. In this paper, we study yet another new FT function which takes the most flexible form of frame-dependent linear transformation. Maximum a posteriori (MAP) estimation is used for estimating FT function parameters to deal with the possible problem of insufficient training data caused by the increased number of model parameters. The effectiveness of the proposed approach is confirmed by evaluation experiments on Finnish Aurora3 database.

We propose novel approaches for optimizing the detection performance in spoken language recognition. Two objective functions are designed to directly relate model parameters to two performance metrics of interest, the detection cost function and the area under the detection-error-tradeoff curve, respectively. Both metrics are approximated with differentiable functions of model parameters by using a smoothing function based on a class misclassification measure. The model parameters are optimized by using the generalized probabilistic descent algorithm. We conduct experiments on the NIST 2003 and 2005 Language Recognition Evaluation corpora. Results show that the proposed approaches effectively improve the performance over the maximum likelihood training approach.

Support vector machine (SVM) equipped with sequence kernel has been proven to be a powerful technique for speaker verification. A number of sequence kernels have been recently proposed, each being motivated from different perspectives with diverse mathematical derivations. Analytical comparison of kernels becomes difficult. To facilitate such comparisons, we propose a generic structure showing how different levels of cues conveyed by speech utterances, ranging from low-level acoustic features to highlevel speaker cues, are being characterized within a sequence kernel. We then identify the similarities and differences between the popular generalized linear discriminant sequence (GLDS) and GMM supervector kernels, as well as our own probabilistic sequence kernel (PSK). Furthermore, we enhance the PSK in terms of accuracy and computational complexity. The enhanced PSK gives competitive accuracy with the other two kernels. Fusing all the three kernels yields an EER of 4.83 % on the 2006 NIST SRE core test. Index Terms: speaker verification, characteristic vector, support vector machine, sequence kernel

We propose to use a new feature transformation (FT) function to construct supervectors of support vector machines for speaker recognition. Considering that estimation of bias vectors is more robust than that of transformation matrices, we define the FT function in a flexible form that transformation matrices and bias vectors are controlled by separate regression classes. Unlike the MLLR-based approach that needs a continuous speech recognition system, our FT function parameters are estimated based on a Gaussian mixture model (GMM). An iterative training procedure is used to achieve the maximum a posteriori estimation of the FT function parameters, which avoids the possible numerical problem caused by insufficient training data in the maximum likelihood estimation. Our approach is evaluated on the SRE2006 NIST evaluation and obtains better performance than a conventional SVM system based on GMM mean supervectors.

The performance of spoken language recognition system is typically formulated to reflect the detection cost and the strategic decision points along the detection-error-tradeoff curve. We propose a performance metrics optimization (PMO) approach to optimizing the detection performance of Gaussian mixture model classifiers. We design the objective functions to directly relate the model parameters to the performance metrics of interest, i.e., the detection cost function and the area under the detection-error-tradeoff curve. Both metrics are approximated by differentiable functions of model parameters. In this way, the model parameters can be optimized with the generalized probabilistic descent algorithm, a typical discriminative training technique. We conduct the experiments on the NIST 2003 and 2005 Language Recognition Evaluation corpora. The experimental results show that the PMO approach effectively improves the performance over the maximum-likelihood training approach.

In the past several years, we've been studying feature transformation approaches for robust automatic speech recognition (ASR) based on the concept of stochastic vector mapping (SVM) to compensating for possible "distortions" caused by factors irrelevant to phonetic classification in both training and recognition stages. Although we have demonstrated the usefulness of the SVM-based approaches for several robust ASR applications where diversified yet representative training data are available, the performance improvement of SVM-based approaches is less significant when there is a severe mismatch between training and testing conditions. In this paper, we present a maximum likelihood approach to unsupervised online adaptation (OLA) of SVM function parameters on an utterance-by-utterance basis for achieving further performance improvement. Its effectiveness is confirmed by evaluation experiments on Finnish AuroraS database.

In this paper we propose a generalized feature transformation approach to compensating for channel variation in speaker verification (SV) applications. Channel-dependent (CD) piecewise linear transformations are used for feature compensation. CD transformation parameters are estimated together with a channel-independent (CI) root Gaussian mixture model (GMM) from training data with a variety of channel conditions by using a maximum likelihood criterion. Experiments are conducted on the 2005 NIST Speaker Recognition Evaluation (SRE) corpus for several text-independent GMM-based SV systems. Experimental results show that the proposed approach achieves relative equal error rate (EER) reductions of 8.19% and 26.24% in comparison with a traditional feature mapping approach and a baseline system, respectively.

In our previous works, a Segmental Switching Linear Gaussian Hidden Markov Model (SSLGHMM) was proposed to model "noisy" speech utterance for robust speech recognition. Both ML (maximum likelihood) and MCE (minimum classification error) training procedures were developed for training model parameters and their effectiveness was confirmed by evaluation experiments on Aurora2 and Aurora3 databases. In this paper, we present an ML approach to unsupervised online adaptation (OLA) of SSLGHMM parameters for achieving further performance improvement. An important implementation issue of how to initialize the switching linear Gaussian model parameters is also studied. Evaluation results on Finnish Aurora3 database show that in comparison with the performance of a baseline system based on ML-trained SSLGHMMs, unsupervised OLA yields a relative word error rate reduction of 4.3%, 9.1%, and 17.8% for well-matched, medium-mismatched, and high-mismatched conditions respectively.

This paper presents a method to extract tone relevant features based on pitch flux from continuous speech signal. The autocorrelations of two adjacent frames are calculated and the covariance between them is estimated to extract multi-dimensional pitch flux features. These features, together with MFCCs, are modeled in a 2-stream GMM models, and are tested in a 3-dialect identification task for Chinese. The pitch flux features have shown to be very effective in identifying tonal languages with short speech segments. For the test speech segments of 3 seconds, 2-stream model achieves more than 30% error reduction over MFCC-based model.

The fundamental issue of the automatic language identification is to explore the effective discriminative cues for languages. This paper studies the fusion of five features at different level of abstraction for language identification, including spectrum, duration, pitch, n-gram phonotactic, and bag-of- sounds features. We build a system and report test results on NIST 1996 and 2003 LRE datasets. The system is also built to participate in NIST 2005 LRE. The experiment results show that different levels of information provide complementary language cues. The prosodic features are more effective for shorter utterances while the phonotactic features work better for longer utterances. For the task of 12 languages, the system with fusion of five features achieved 2.38% EER for 30-sec speech segments on NIST 1996 dataset.

We present a speaker recognition system with multiple GMM tokenizers as the front-end, and vector space modeling as the back-end classifier. GMM tokenizer captures the acoustic and phonetic characteristics of a speaker from the speech without the need of phonetic transcription. To enhance the speaker characteristics coverage and provide more discriminative information, a speaker clustering algorithm is proposed to build multiple GMM tokenizers that are arranged in parallel. For an input utterance, each of the tokenizers outputs a token sequence, which is then represented by a vector of n-gram probabilities. Multiple vectors are concatenated to form a composite vector. Finally the Support Vector Machine (SVM) is used as the back-end classifier of the composite vectors. We use the 2002 NIST Speaker Recognition Evaluation (SRE) corpus for training GMM tokenizers and background modeling, and evaluate on the 2001 NIST SRE corpus. Index Terms: speaker recognition, speaker clustering, GMM tokenization

Several recent approaches for robust speech recognition are developed based on the concept of stochastic vector mapping (SVM) that perform a frame-dependent bias removal to compensate for environmental variabilities in both training and recognition stages. Some of them require stereo recordings of both clean and noisy speech for the estimation of SVM function parameters. In this paper, we present a detailed formulation of a maximum likelihood training approach for the joint design of SVM function parameters and HMM parameters of a speech recognizer that does not rely on the availability of stereo training data. Its learning behavior and effectiveness is demonstrated by using the experimental results on the Aurora3 Finnish connected digits database recorded by using both close-talking and hands-free microphones in cars.

Mel-frequency cepstral coefficients (MFCCs) are the most widely used features for speech recognition. These are derived from the power spectrum of the speech signal. Recently, the cepstral features derived from the modified group delay function (MGDF) have been studied by Murthy and Gadde (Proc. ICASSP, vol.1, p.68-71, 2003) for speech recognition. In this paper, we propose to use the product of the power spectrum and the group delay function (GDF), and derive the MFCCs from the product spectrum. This spectrum combines the information from the magnitude spectrum as well as the phase spectrum. The MFCCs of the MGDF are also investigated in this paper. Results show that the cepstral features derived from the power spectrum perform better than that from the MGDF, and the product spectrum based features provide the best performance.

Sub-band speech recognition approaches have been proposed for robust speech recognition, where full-band power spectra are divided into several sub-bands and then likelihoods or cepstral vectors of the sub-bands are merged depending on their reliability. In conventional sub-band approaches, correlations across the sub-bands are not modeled and the merging weights can only be set experientially or estimated during training procedures, which may not match observed data. The methods further degrade performance for clean speech. We proposed a novel sub-band approach, where frequency sub-bands are multiplied with weighting factors and merged, which considers sub-band dependence and proves to be more robust than both full-band and conventional sub-band approaches. And further the weighting factors can be obtained by using the maximum-likelihood estimation approaches in order to minimize the mismatch between the trained models and the observed features. Finally we evaluated our methods on both the Aurora2 task and the Resource Management task and showed improvement of performance on the two tasks consistently. 1.